| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849 |
- ## @package bpr_loss
- # Module caffe2.python.layers.bpr_loss
- from caffe2.python import schema
- from caffe2.python.layers.layers import (
- ModelLayer,
- )
- from caffe2.python.layers.tags import (
- Tags
- )
- import numpy as np
- # ref: https://arxiv.org/pdf/1205.2618.pdf
- class BPRLoss(ModelLayer):
- def __init__(self, model, input_record, name='bpr_loss', **kwargs):
- super(BPRLoss, self).__init__(model, name, input_record, **kwargs)
- assert schema.is_schema_subset(
- schema.Struct(
- ('pos_prediction', schema.Scalar()),
- ('neg_prediction', schema.List(np.float32)),
- ),
- input_record
- )
- self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
- self.output_schema = schema.Scalar(
- np.float32,
- self.get_next_blob_reference('output'))
- def add_ops(self, net):
- # formula:
- # loss = - SUM(Ln(Sigmoid(Simlarity(u, pos) - Simlarity(u, neg))))
- neg_score = self.input_record.neg_prediction['values']()
- pos_score = net.LengthsTile(
- [
- self.input_record.pos_prediction(),
- self.input_record.neg_prediction['lengths']()
- ],
- net.NextScopedBlob('pos_score_repeated')
- )
- # https://www.tensorflow.org/api_docs/python/tf/math/log_sigmoid
- softplus = net.Softplus([net.Sub([neg_score, pos_score])])
- net.ReduceFrontSum(softplus, self.output_schema.field_blobs())
|
